Fault diagnosis network design for vehicle on-board equipments of high-speed railway: A deep learning approach

With the rapid development of high-speed railways (HSRs) throughout the world, the fault diagnosis systems of vehicle on-board equipments (VOBEs) for high speed trains have received increasing attention. Since the faults of VOBEs in HSRs are usually uncertain and complex, the current fault diagnosis methods are mainly based on manual judgement in real-world operations, which is generally inefficient and insecurity with the big rail traffic data. In this paper, we propose an automated diagnosis network of VOBE for high-speed train via a deep learning approach. First, we propose a mathematical model to formulate the fault diagnosis problem in HSRs, involving the definition of fault evidence vectors and reason vectors by analyzing the real-world fault data that are collected in Wuhan-Guangzhou high speed railway. Then, a deep belief network (DBN) and its training procedures are developed on the basis of Restricted Boltzmann Machine (RBM). Finally, the proposed diagnosis network is trained and validated with real-world data. Furthermore, we compare the DBN-based fault diagnosis network with k-nearest neighbor (KNN) and ANN-BP (artificial neural network with back propagations). The results indicate that, the developed DBN outperforms both KNN and ANN-BP, and improves the accuracy of fault diagnosis for VOBEs to 90-95% in HSRs.